在预防和诊断口腔状细胞癌的范式转变
Jan-Michaél Hirsch1,2, Ronak Sandy3, Bengt Hasséus4
1Department of Surgical Sciences, Oral and Maxillofacial Surgery, Medical Faculty, Uppsala University, Uppsala, Sweden.
概括
早期发现口腔癌 (OSCC) 可以提高生存率. 这项研究探讨了使用人工智能和刷活检的基因组分析进行非侵入性查,以识别高风险病变并预测癌症转变.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 人工智能的人工智能
背景情况:
- 口腔状细胞癌 (OSCC) 的5年生存率很低 (50%-60%).
- 患有潜在恶性口腔病变的个人面临患OSCC的高风险.
研究的目的:
- 突出非侵入性方法来识别患有恶性转变风险的口腔粘膜病变.
- 通过可靠的风险人群查,使OSCC的早期诊断成为可能.
主要方法:
- 未来的多中心研究涉及患有白血病的患者.
- 使用非侵入性刷活检进行细胞学诊断和基因组分析.
- 采用全幻灯片成像和深度神经网络用于人工智能驱动的细胞表征.
- 进行单细胞RNA测序,绘制质细胞转录组图和识别生物标志物.
主要成果:
- 启用了整个角质细胞转录组的映射.
- 基于mRNA表达的预测癌症转变的生物标志物的简化识别.
- 证明了人工智能和基因组分析对客观细胞特征的潜力.
结论:
- 非侵入性方法可以使早期的OSCC诊断变得可及.
- 针对性治疗的生物标志物识别对于改善患者的治疗结果至关重要.
- 大规模的样本采集,基因组分析和机器学习对OSCC管理充满希望.
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